Abstract
This research project investigates the relationship between regional demographic composition and voting behaviour in the context of the 2023 Swiss federal election. With the Swiss People’s Party (SVP) regaining its electoral strength by securing 27.9%, this study explores how demographic factors, particularly the percentage of non-Swiss residents in a particular area, correlate with SVP support. The analysis includes other demographic indicators, such as education level, age, income per capita, and naturalisation rates, to control for confounding variables. The findings reveal that municipalities with lower proportions of non-Swiss residents and higher shares of individuals with secondary-level education (e.g., vocational training) exhibited significantly stronger support for the SVP. In contrast, regions with higher shares of foreign-born residents, greater educational attainment, and higher naturalisation rates were more likely to favour left-leaning parties, such as the Social Democratic Party (SP) and the Greens. While the study highlights strong correlations, it does not establish clear causation, and regional variations persist. These insights provide valuable guidance for policymakers, political organisations, and civil society groups aiming to address political polarisation and promote inclusive democratic participation.
Switzerland, known for its political stability, has a unique system
of governance defined by direct democracy, a bicameral parliament, and
strong decentralised federal institutions. Since the establishment of
the modern Swiss state in 1848, citizens have not only elected
representatives but also enacted policies through referendums and
initiatives. Excluded from these processes is roughly one in four
residents of Switzerland, since they do not hold Swiss citizenship,
despite significantly contributing to Swiss society and the economy. Up
until today, migrant populations remain largely excluded from federal
decision-making processes, even multiple decades after their parents,
grandparents, or even great-grandparents came to Switzerland.
The far-right Swiss People’s Party (SVP) finds itself at the centre
of many ongoing political debates, having been a dominant political
force for decades and having gained attention internationally for its
hardline opposition to immigration, religious diversity, and social
liberal issues like feminism and LGBT politics. The 2023 federal
elections marked a significant moment, with the SVP coming back full
force, receiving 27.9% of the votes compared to the considerably weaker
result of 25.6% in 2019. This renewed electoral momentum — apparently
driven by anti-migration rhetoric—calls for a more thoughtful
examination: To what extent does demographic composition—particularly
the proportion of non-Swiss residents — shape political preferences
across Swiss municipalities? Are ethnoculturally homogeneous regions
more likely to endorse far-right ideologies compared to diverse, urban
centres?
This project seeks to explore these relationships by looking more closely at the 2023 Swiss federal election results alongside key demographic indicators, including citizenship acquisition rates, income levels, educational attainment, and age distribution. The primary recipients and potential clients of this study are political consultants, political parties, and organisations researching political radicalisation and polarisation. Understanding the demographic and socioeconomic factors related to the SVP’s success offers important insights for improved political communications, campaign strategies, and social initiatives. Understanding what drives voting behaviour and why certain messaging mobilises voters supports political discourse and offers an option for addressing both anxieties and aspirations. Also, by recognising regions where demographic factors correlate with lower far-right support, parties can strengthen alliances and mobilise underrepresented groups, ensuring a more balanced and representative democratic landscape and a lower risk of political radicalisation and social polarisation.
Switzerland has a long tradition of democracy, with its modern
political system rooted in the 1848 Federal Constitution. Along with its
constitution, the bicameral legislative system, was established (Church, 2013, Chapter 6). Swiss elections take
place every four years, with the federal government following a system
of direct democracy that allows citizens not only to elect
representatives but also to participate in referendums and initiatives.
As mentioned before, Switzerland’s Federal Parliament consists of two
chambers: the National Council (200 members) and the Council of States
(46 members). The National Council represents the population
proportionally, while the Council of States represents the cantons, with
each full canton electing two representatives and six half-cantons
electing one. This bicameral system aims to balance demographic and
regional interests in legislative decisions.
According to the annual overview of the Federal Statistical Office
(BfS, 2023), Switzerland has a population
of 8.8 million people (p. 132), of whom around 74% hold Swiss
citizenship. The remaining 26% are non-Swiss residents, including
permanent residents, cross-border workers, and asylum seekers (p. 142).
Swiss nationality is acquired by most citizens through descent and by
migrants through naturalisation, a decentralised process primarily
reliant on cantonal and municipal approval. Only a fraction of foreign
nationals apply for naturalisation—between 30,000 and 45,000 people per
year over the last decade — which corresponds to around 2% of the
population eligible for naturalisation (p. 142).
Non-Swiss residents, despite their significant share of the
population, have limited political rights at the federal level. Some
cantons and municipalities allow foreigners to vote in local elections
or even run for office, but they are largely excluded from national
decision-making (jura.ch, 2025; ne.ch,
2025). The politics of Switzerland are described as relatively
polarised compared to international standards (Jansen & Stutzer, 2024, p. 3).
While many European countries experienced an extraordinary rise in
right-wing politics throughout the 2010s, Switzerland remained
relatively stable. The right-wing SVP (Schweizerische Volkspartei; in
French: Union démocratique du centre, UDC) has been part of the
governing coalition since the 1990s. Certain segments of the party have
been classified as far-right and right-wing extremist, most notably due
to the party’s hardline positions on migration and its strict opposition
to legal protections for social, cultural, and religious minorities,
such as racially-discriminated minorities, Swiss muslims or LGBT
citizens (Ellermann, 2021, p. 3 & p. 102;
Jansesberger & Rhein, 2024, pp. 3–5).
The relationship between regional demographic composition and voting
behaviour has been a subject of ongoing research and debate,
particularly regarding whether areas with lower proportions of migrants
tend to support stricter immigration policies compared to
ethnoculturally diverse regions. In this study, the most recent Swiss
election is examined to analyse potential correlations between the
percentage of non-Swiss residents and the electoral performance of the
Swiss People’s Party (SVP), which centred its campaign on an
anti-immigration platform. To control for confounding variables, other
demographic markers, such as age and education, will be included.
The underlying hypothesis posits a positive correlation between
ethnocultural homogeneity and the electoral success of the far-right
Swiss People’s Party (SVP) in the 2023 Swiss federal elections.
Specifically, municipalities with a higher proportion of foreign
residents are expected to exhibit lower support for the SVP than those
with a more homogeneous Swiss population.
Defining what constitutes a “high” or “low” proportion of the
non-Swiss population is inherently subjective. However, current
demographic data indicate that 25% to 27% (depending on method and
definitions) of Switzerland’s total population is non-Swiss, with
canton-level proportions ranging from 12.9% (Appenzell Innerrhoden) to
42.2% (Geneva). Based on this distribution, a low share of the migrant
population can be approximated as below 20%, while a high share is
anything above 30%.
To better understand the multitude of factors influencing electoral
behaviour, additional variables are incorporated into the analysis,
including the citizenship acquisition rate, taxable income per capita,
educational attainment levels, and age distribution. These factors help
to contextualise the observable effects and differentiate between
correlation and causation. Furthermore, to comprehensively examine the
SVP’s 2023 electoral success, the analysis includes other political
parties to highlight electoral dynamics and explore how voter
distributions vary across demographic markers. The derived research
question is as follows: “Which demographic factors exhibit a strong
relationship with the 2023 Swiss federal election results?” This
question can be further divided into examining specific aspects between
parties and their respective electoral performance.
All datasets used in this analysis were obtained from the Swiss
Federal Office of Statistics (BFS). Each dataset included the BFS
municipality ID, with the exception of the dataset on education levels
by district, which lacked an ID. Matching this dataset by district name
using regular expressions was not feasible due to inconsistencies in
district naming conventions. To address this, district numbers were
manually added to the table. Aside from this exception, no further
modifications were made to the datasets.
| Dataset Description | Dataset ID |
|---|---|
| Election Results 2023 | sd-t-17.02-NRW2023-parteien-appendix.csv |
| Citizenship Pecentage | px-x-0102010000_104_20250127-155044.xlsx |
| Education | su-e-40.02.15.08.05-2022.xlsx |
| Citizenship acquisition | px-x-0102020000_201_20250129-134648.xlsx |
| Age distribution | su-d-01.02.03.06.xlsx |
| Some income or wealth metric TBD | 27600_131.xlsx |
| Datatable Communes, Districts and Cantons | Gemeindestand.xlsx |
The process commenced with the specification of a research question
and hypothesis, establishing the conceptual foundation for the project.
Following this, the necessary datasets were identified and acquired from
the BFS webpage. The initial phase involved exploratory analysis through
basic linear models and correlation assessments, serving as a
preliminary validation of the research direction. As the project
progressed, increasing levels of complexity were introduced,
incorporating statistical modelling techniques and advanced
visualisations to capture the relationship between demographic
indicators, regional dimensions, and electoral outcomes in greater
depth.
Generative AI was utilised in a limited capacity, primarily for
debugging specific error messages in R and refining textual content. In
terms of text composition, its primary use was for the correction of
grammar and spelling errors, as well as stylistic improvements within
certain paragraphs. Beyond these functions, the technology was not
employed. Although generative AI can be useful for speeding up the
writing process, most texts generated or corrected by these systems have
a generic tone and are immediately recognisable as such. This is why the
text always needs to be rewritten and refined. So, while it helps reduce
grammar mistakes and inconsistencies, it does not replace the writing
process.
Over the past several decades, the distribution of dominant political
parties in Swiss national elections has remained relatively stable. The
primary parties represented in the federal parliament, along with their
respective campaign focuses in the most recent elections, are detailed
below (bpb.de, 2023). For improved
readability, this paper will rely on German-language party names.
| Party | 2023 Campaign Focus |
|---|---|
| SVP | Right-wing, anti-immigration, anti-welfare, free market policies |
| SP | Left-wing, pro-welfare, pro-worker policies, reducing cost of living |
| FDP | Center-right, free market policies and improved access to international markets |
| Die Mitte | Conservative centrist, pro-defense, tax cuts for married couples |
| GLP | Progressive centrist, climate protection, EU alignment, liberal market policies |
| GPS | Left-wing, climate protection, biodiversity, state regulation of business |
Regional parties, such as the far-right Lega in the Canton of Ticino
or the Mouvement Citoyen Romand MCR in Geneva, have been taken into
account for some analyses. However, due to their regionality, the data
for these parties are scarce and might not yield highly reliable
results.
The most recent parliamentary elections in Switzerland took place on
22 October 2023. Switzerland’s bicameral parliament comprises the
National Council, which proportionally represents the Swiss population
with seats allocated to each canton based on its population, and the
Council of States, where all cantons have equal representation. As the
National Council reflects the population proportionally, its election
results are often regarded as an indicator of trends in public opinion
regarding politics and policy.
As of 1 January 2024, Switzerland has 2,131 registered
municipalities, almost 100 fewer than the total of 2,212 in 2019. The
analysis included only the municipalities that could be matched
consistently across both elections, which comprised exactly 2,113
municipalities, meaning their municipality ID remained the same from
2019 to 2024. Even though a range of 2,113 to 2,212 municipalities seems
like a significant discrepancy, most municipalities affected by mergers
or dissolution do not impact the outcome of the weighted correlation, as
their population is, in almost all cases, below 1,000 and, in many
cases, below 100. For example, the village of Corippo TI was merged in
2019 into a neighbouring community but had only 9 inhabitants at the
time of the merger. The disappearance of such small municipalities due
to mergers does not affect the outcome of the analysis in any
significant way (BfS, 2025c).
All the data points (apart from education level) were available at
the municipality level. To calculate the corresponding values for each
canton, the municipal values were grouped by canton and weighted by the
population size of the municipality in the case of demographic values,
or by vote numbers in the case of election results. In the same way, a
total for all of Switzerland was calculated and included in some plots
to better compare the cantons not only in relation to one another but
also to the Swiss average values.
For this analysis, the focus was on the five demographic indicators
below (see Table). These five indicators are of particular interest for
measuring their influence on far-right politics, as previous research
has shown them to be predictors of far-right party success.
| Indicator | Descrption | Metric |
|---|---|---|
| Non-Swiss Population | People residing in Switzerland without holding Swiss Citizenship | % of total population |
| Citizenship Aquisition | Rate of Migrants acquiring Swiss citizenship in 2023 | % of migrant population |
| Income per Capita | Normalized taxable income. | in Swiss Francs |
| Education Level | Highest educational achievement. | 3 Grades (Low, Secondary, Tertiary) |
| Age Ratio | Percentage of people aged >64 in relation to population aged 18-64 | % of working age population |
The indicator “Non-Swiss Population” represents the percentage of
citizens holding a passport other than the Swiss one in 2023, relative
to the total population. Migrants who have acquired Swiss citizenship
count as part of the Swiss population and are not disclosed or analysed
separately.
The indicator “Citizenship Acquisition” represents the percentage of
citizens acquiring a Swiss passport in 2023 relative to the total
migrant population. The rate of absorption of foreign nationals into
Swiss society reflects both the willingness of migrants to become Swiss
citizens and the willingness of local communities to support the
non-Swiss population’s integration at the citizenship level.
Income per capita is a normalised measure used by the Federal
Statistical Office to compare income across Switzerland. It represents
the income (minus deductibles at the federal level) recorded by the
federal tax office in collaboration with the cantonal tax
administrations. Therefore, it does not represent net income before
deductibles which would be higher.
Education levels, usually measured by the highest achieved school degree, are crucial for the economic and social development of populations. They significantly impact individuals’ perceptions of life and political leanings. The three indicators are as follows:
The age ratio is a standardised measure used by the Federal
Statistical Office to determine the percentage of the aged population
(65 and above) relative to the adult working-age population (18–64).
Therefore, a high number indicates a disproportionately aged
population.
Initially, the correlation between the migrant population and
far-right election success was calculated both weighted and unweighted,
meaning each municipality counted as one unit regardless of population
size. The unadjusted analysis did not produce a significant correlation,
presumably because both left-wing voters and migrant populations tend to
be concentrated in larger cities and therefore could not be adequately
represented in an unweighted statistical design. However, when adjusted
for population size, clear correlations became visible.
The statistical analysis relies on correlation and linear models. In
order to improve the model’s performance, the demographic indicators
were scaled and weighted. Scaling enhances interpretability, numerical
stability, and the measurability of smaller differences while mitigating
the influence of outliers. Weighting was necessary for practical
reasons, as the size of a municipality can also be a predictor of
election outcomes. If every municipality were counted as one unit, this
would suppress the outcomes of larger cities.
Most of the plots did not include statistical models but rather
visualised the election outcomes without modelling.
This project leverages interactive mapping capabilities to visualize
Swiss electoral results data. While packages like dplyr and ggplot2 were
covered in the main course, we specifically focus on the leaflet package
for creating interactive web maps - a specialized tool for geographical
data presentation that offers new possibilities for data visualization.
This approach combines familiar data manipulation techniques with
advanced spatial data handling through leaflet’s
interactive features and the sf (simple features) package’s
geospatial capabilities.
The implementation utilizes several specialized R packages:
leaflet: For creating interactive web mapssf: For handling spatial vector datahtmltools: For HTML widget generationraster: For spatial data processingThese packages extend beyond basic data visualization to create
dynamic, web-based visualizations that allow to explore electoral data
through an interactive interface.
The maps integrate various spatial data levels including municipality
boundaries, canton boundaries, Lakes (for improved geographical
context), country outline. Incorporated interactive elements are hover
tooltips showing detailed electoral information, color-coded regions
based on winning parties, Zoom and pan capabilities. The coordinate
system management handles coordinate system transformations, converting
all geographical data to WGS84 (EPSG:4326) for web compatibility.
The interactive elements allow to explore both broad patterns and specific local results, representing a significant advance over static visualizations. This chapter demonstrates two distinct mapping approaches:
Canton-Level Analysis: Shows winning parties by canton with simplified boundaries, offering a broad overview of regional political trends.
Municipality-Level Detail: Provides granular insight into local voting patterns, revealing nuanced geographical patterns that might be missed in aggregate data.
Several technical challenges were addressed. Merging electoral data
with geographical boundaries required careful handling of identifiers
and coordinate systems (Data Integration). The maps balance detail with
performance, managing the complexity of displaying over 2,000
municipalities while maintaining responsiveness (Performance
Optimization). Careful consideration was given to color schemes and
opacity levels to ensure readability while maintaining aesthetic appeal
(Visual Clarity).
This mapping approach adds several valuable dimensions to the
analysis. One focus was on the spatial context, in order to reveals
geographical patterns in voting behavior that might be missed in
traditional statistical analysis. Interactive elements allow readers to
explore data according to their interests, improving user engagement.
Further the maps effectively communicate multiple data dimensions
(winning party, vote share, geographical location) in an intuitive
format.
In order to better understand the results of the analysis, we
assessed the dataset. The main goal was to gain an understanding of the
existing trends and distributions to explain why certain effects might
cause correlations or regressions.
The graph visualises the proportion of non-Swiss citizens per canton
as well as for Switzerland as a whole. This information is significant,
as previous studies (Tresch et al., 2024, p.
15) show that areas without large migrant populations tend to
vote for parties running anti-migration campaigns. This highlights an
important contradiction: people who are not exposed to migrant
populations tend to be more critical of migration than voters in areas
with higher ethno-cultural diversity.
The graph also visualises the proportion of different educational
levels within the population. We know from other studies (BfS, 2025b, 2025a) that most Swiss people hold
a secondary education degree, leading us to assume that the demographic
segment possessing only obligatory education might include a high
percentage of non-Swiss populations, particularly refugees who could not
complete their schooling due to displacement caused by war and conflict.
Therefore, the Swiss population can be roughly divided into those with
secondary and tertiary education. The segment with secondary-level
education is of particular interest, as it predominantly comprises Swiss
citizens and Swiss-born migrants, with or without Swiss citizenship, who
did not pursue further education after completing their professional
qualifications. Prior studies on this topic suggest that this group may
be more likely to vote in favour of the far-right party SVP and less
likely to support left-wing parties like the SP and Greens, as well as
centrist parties like the GLP and FDP. From the graph, we can observe
some consistency in education levels across Switzerland, with
fluctuations ranging from 30% on the lower end to 50% on the higher
end.
The stacked bar chart illustrates the distribution of education
levels across Swiss cantons, displaying the weighted percentage of
populations with low, secondary, and tertiary education. Each bar
represents a canton, with CH on the left representing the national
average. The chart reveals significant variation in education levels
across different regions. Some cantons, such as Zurich (ZH), Geneva
(GE), and Zug (ZG), show a higher proportion of tertiary education,
reflecting their strong economic and academic infrastructure. In
contrast, cantons like Appenzell Innerrhoden (AI), Uri (UR), and
Obwalden (OW) have a higher share of secondary education and lower
tertiary education levels, aligning with the prevalence of vocational
training in these regions. The share of low education varies but tends
to be more pronounced in rural or traditionally working-class
cantons.
The boxplots presented in this visualisation illustrate the
distribution of taxable income per capita across Switzerland, both at
the national level and disaggregated by canton.
In the consolidated Swiss boxplot, the median income per capita
appears to be around CHF 31’520. The top 1 percentile outliers were
filtered out because they skewed the boxplot, but still the presence of
numerous high-income outliers suggests a concentration of wealth in
certain regions and thus indicating significant income disparities
across Swiss municipalities. The whiskers extend to approximately 1.5
times the interquartile range, beyond which extreme values are plotted
as individual points. The overall distribution indicates that while the
majority of municipalities cluster within a similar income range, a
subset of regions demonstrates exceptionally high taxable incomes,
potentially influencing the national economy.
The second boxplot presents income per capita at the cantonal level.
Certain cantons, such as Zug, Schwyz, and Geneva, exhibit higher median
incomes and a greater number of extreme outliers, indicating the
presence of high-income municipalities. Conversely, cantons such as
Jura, Valais, and Uri display significantly lower median taxable
incomes.
The variation in box sizes also indicates differing levels of income
inequality within each canton, with some exhibiting a narrower
interquartile range, while others display a broader distribution of
incomes. For example Geneva showing by far the highest variance while
some cantons seem to have very little variance (Glarus, Jura and
Schaffhausen).
Switzerland held federal elections to renew the National Council and
the Council of States on 22 October 2023. Leading up to the 2023
elections, there was considerable anticipation regarding whether the
environmental momentum observed in 2019 would persist. The 2019
elections saw significant gains for parties that centred their policies
around pro-environmental messaging, most notably the Greens and GLP.
However, the results indicated a shift in voter priorities, with the
SVP’s focus on migration seemingly resonating more with the
electorate.
The Swiss People’s Party (SVP), known for its anti-migration stance
since gaining momentum in the 1990s, reinforced its image as the party
of choice for a voter base sceptical of migration. In 2023, the party
achieved significant gains, reversing the losses it experienced in the
2019 elections. In contrast, both the Green Party and the Green Liberal
Party faced notable setbacks, losing a considerable portion of the seats
they had won in 2019.
This map visualises the winning political party in each Swiss canton based on the 2023 federal election results. Each canton is colour-coded to represent the party with the highest vote share. The map shows a clear regional divide, with the SVP dominating in central and eastern Switzerland, the SP winning in the western, predominantly French-speaking cantons, and the Mitte party prevailing in some central and southern regions. The FDP’s dominance is visible in Ticino.
This map expands on the first by including both the first and second
most popular parties per canton. It introduces the Greens (GRUENE). The
SP and SVP remain the dominant players, with the Mitte and FDP securing
strongholds in central and southern regions.
This detailed map displays the winning party in each Swiss municipality, offering a granular view of the 2023 election results. The SVP dominates rural and less densely populated areas, particularly in the German-speaking parts of Switzerland.
The SP and the Greens hold the majority in urban centres such as Geneva, Lausanne, Bern, Zurich, and Basel. Regional parties like Lega in Ticino and MCG in Geneva also appear prominently now. This municipal-level analysis reinforces the pattern seen at the cantonal level, where urban areas favour left-leaning parties while rural regions remain conservative strongholds.
This chapter’s plots visualise the 2023 election results against
demographic factors, as well as, in the first plot below, against the
change in vote share compared to 2019 (y-axis) across Swiss
municipalities. Each point represents a municipality, with colour
indicating the canton and size reflecting the municipality’s
population.
In the first plot, a positive y-value signifies a gain in SVP
support, whereas a negative y-value indicates a decline in election
performance. Most municipalities show a slight gain, but extreme shifts
(both gains and losses) are scattered. The colour distribution reveals
regional variations, highlighting how different cantons experienced
varying levels of SVP growth or decline.
The second scatter plot examines the relationship between the SVP’s
vote share in 2023 (x-axis) and the percentage of non-Swiss residents in
each municipality (y-axis), which is central to the research project.
The negative trend in the distribution suggests that municipalities with
a higher share of non-Swiss residents tend to have lower SVP support,
aligning with the hypothesis that ethnocultural homogeneity correlates
with higher far-right support.
Municipalities with low non-Swiss population percentages (below 20%)
exhibit a wide range of SVP vote shares but appear most densely
concentrated around 25–40% of the electorate, while those with high
non-Swiss populations (above 40%) are generally clustered at lower SVP
support levels. Larger municipalities, represented by larger circles,
tend to have higher shares of non-Swiss residents and lower SVP support,
further reinforcing the trend that urban areas with diverse populations
are less inclined to vote for the far-right party.
The visual representation of the data points, without statistical
analysis or any data processing, appears to support the
hypothesis.
The next plot, shown below, illustrates the relationship between the
SVP’s vote share in 2023 (x-axis) and the age quota (y-axis), which
likely represents the proportion of elderly residents in a municipality.
A broad distribution of points suggests no immediate strong correlation,
although municipalities with a lower age quota (below 50%) appear to
exhibit more variability in SVP support, while those with a higher age
quota tend to cluster in the lower-to-mid SVP vote share range (0–40%).
Larger municipalities, indicated by bigger circles, are concentrated at
lower age quotas, suggesting that urban areas may have a younger
demographic. The data highlights regional differences in how age
structure might relate to voting behaviour.
The scatter plot illustrates the relationship between the SVP’s vote
share in 2023 (x-axis) and the naturalisation rate (y-axis) across Swiss
municipalities. The distribution suggests that municipalities with
higher SVP support generally have lower naturalisation rates, as most
points are concentrated near the bottom of the y-axis. There are
relatively few municipalities with both high SVP support and high
naturalisation rates, reinforcing the idea that areas with more frequent
citizenship acquisitions may be less inclined to vote for the far-right
party.
Larger municipalities, represented by bigger circles, tend to have
slightly higher naturalisation rates, possibly due to more diverse
populations and greater administrative capacity for processing
naturalisations. The plot suggests an inverse relationship between
naturalisation rates and SVP support, aligning with the broader
hypothesis that ethnocultural diversity correlates with lower far-right
voting patterns.
The scatter plot illustrates the relationship between the SVP’s vote
share in the 2023 election (x-axis) and the taxable income per capita in
Swiss municipalities (y-axis). The distribution suggests that
municipalities with higher income levels tend to have lower SVP support,
as most high-income areas are clustered on the left side of the plot.
Municipalities with lower income levels show a broader range of SVP vote
shares, but a large concentration of points is visible in the
lower-to-mid range of SVP support. The presence of a few high-income
municipalities with relatively low SVP support reinforces the idea that
wealthier areas may be less inclined to vote for the far-right
party.
Larger municipalities, represented by bigger circles, generally
appear in the lower-to-middle income range, further suggesting that
urban and economically stronger regions have lower SVP support.
The scatter plot displays the relationship between the SVP’s vote
share in the 2023 election (x-axis) and the population size of Swiss
municipalities (y-axis). The population is shown on a logarithmic scale,
allowing for better visibility of both small and large municipalities.
Larger municipalities, represented by bigger circles, tend to have lower
SVP support, while smaller municipalities exhibit a wider range of vote
shares. The clustering of points at lower SVP percentages suggests that
more densely populated areas are generally less supportive of the SVP,
whereas some smaller municipalities show higher levels of support. The
colour variations indicate different cantons, reflecting regional
differences in electoral behaviour.
The scatter plot examines the relationship between the SVP’s vote
share in the 2023 election (x-axis) and the percentage of people with
secondary education in each district (y-axis). The distribution suggests
a possible positive correlation, as districts with higher SVP support
tend to have a greater percentage of secondary education graduates. In
contrast, districts with lower SVP support show a wider range of
secondary education levels, with some clustering around lower
percentages. Larger circles, representing more populous districts,
appear throughout the graph but seem more concentrated in the lower SVP
vote share range. Overall, this variable appears to be highly
significant in understanding the electoral success of the SVP.
The plot presents regression results across Swiss cantons, showing
the relationship between various demographic factors and the vote shares
of different political parties in the 2023 election. Each facet
represents a canton, with factors such as tax income per capita,
population with secondary education, non-Swiss population share,
naturalisation rate, and age quota displayed on the y-axis. The x-axis
represents the effect size on a log scale, indicating the strength and
direction of the relationship between each factor and party support.
Each coloured dot corresponds to a political party, highlighting how
demographic variables influenced voting behaviour differently across
cantons.
In relation to the SVP’s electoral performance, the distribution of
points suggests that certain factors have consistent effects across
cantons, while others vary significantly. For example, secondary
education and non-Swiss population share appear frequently, reflecting
their integral role as variables associated with the SVP’s electoral
success. A high share of the population with a secondary-level degree
(Berufslehre, Apprentissage) shows a strong influence on the SVP’s
success, while a high percentage of the non-Swiss population is
associated with weaker electoral performance. Across Switzerland, both
effects seem equally strong, indicating that a key voter group for the
SVP comprises Swiss nationals without university education in areas
without significant migrant populations.
The other predictors show high variability across cantons. For
example, income per capita can be positively associated with the SVP’s
electoral success in cantons like Obwalden and Nidwalden, while it is
strongly associated with weaker SVP performance in high-income
populations, such as in Solothurn and Thurgau. Similar variability can
be seen with the age quota and naturalisation rate.
The Social Democratic Party (SP) performs better in municipalities
with a higher percentage of non-Swiss residents, a higher naturalisation
rate, and lower taxable income per capita. Its support tends to decline
in areas with a strong presence of secondary education graduates and
older populations, suggesting that ethnocultural diversity,
university-educated populations, and policies addressing younger
people’s needs align with the SP’s electoral success.
The Mitte party shows stronger electoral performance in
municipalities with higher secondary education levels and moderate
taxable income per capita. It tends to perform worse in areas with
either a very high or very low share of non-Swiss residents and lower
naturalisation rates, indicating that its voter base resides in
agglomerations with an average percentage of migrant populations and
represents a middle-class demographic.
The Green Party performs well in municipalities with a high
percentage of non-Swiss residents, high naturalisation rates, and lower
taxable income per capita, aligning with its progressive stance on
migration and environmental policies. The low-income component is
particularly interesting and seems contradictory to the Greens’
stronghold municipalities, which include economically developed cities
such as Bern, Zurich, Basel, and Geneva. This could be influenced by the
Greens’ voter base in rural areas like Valais, Jura, or Graubünden, or
by the fact that younger populations who might vote for the Greens have
not yet reached higher income brackets. The Greens tend to struggle in
areas with a strong secondary education presence and older populations,
suggesting that younger, urban voters form its primary support
base.
The Green Liberal Party (GLP) gains support in municipalities with
higher taxable income per capita and a strong presence of secondary
education graduates, highlighting strong backing in agglomerations and
possibly among self-employed professionals or SMEs. It tends to perform
worse in areas with lower naturalisation rates and fewer non-Swiss
residents, suggesting that its voter base consists of educated, urban
professionals.
The Free Democratic Party (FDP) performs best in municipalities with
high taxable income per capita and a strong secondary education
presence, indicating that SMEs, self-employed upper-class individuals,
and higher middle-class populations are its key voter base.
Two cantonal parties also merit attention, particularly regarding
their local performance. Lega in Ticino shows a strong positive
correlation with lower taxable income per capita and lower secondary
education levels, indicating that it performs well in economically
weaker areas with lower educational attainment. It is also positively
associated with municipalities that have a higher share of non-Swiss
residents and higher naturalisation rates, suggesting that the party
gains support in more diverse regions where migration issues may be
politically relevant. The Mouvement Citoyens Genevois (MCG) in Geneva is
negatively correlated with taxable income per capita, secondary
education, non-Swiss population share, and naturalisation rates,
suggesting that it performs poorly in wealthier and well-educated
municipalities. Its support decreases in areas with a higher proportion
of foreign residents. Both Lega and MCG seem to share some voter base
characteristics, but at the cantonal level, there appears to be
segmentation, with visible differences in the indicators of the SVP’s
electoral success compared to Lega or MCG.
Two of the indicators - income per capita and education - seem to have a particularly interesting regional variance Below, their influence according to the regression model is mapped.
This map presents the scaled income per capita across Swiss cantons, with blue representing higher-income regions and red indicating lower-income areas. The wealthiest cantons include Zug, Zurich, and Geneva, while lower-income regions are concentrated in the Jura, Valais, and parts of central Switzerland. Higher-income regions tend to lean towards economically liberal parties like the FDP and GLP, while lower-income areas show stronger support for the SVP and cantonal parties like Lega and MCG. This aligns with the broader trend of wealthier urban areas favouring centrist and progressive policies, while economically weaker regions lean towards populist and conservative platforms.
This map illustrates the scaled education factor by canton, with colours ranging from red (lower education levels) to blue (higher education levels). Higher education levels are most prominent in cantons like Zurich, Geneva, and Vaud, while lower levels are found in rural cantons such as Uri, Obwalden, and Appenzell Innerrhoden. The map highlights the correlation between education and political preferences, as cantons with higher education attainment tend to support left-leaning parties (SP, Greens), while those with lower education levels align more with the SVP and other conservative parties.
Procedural limitations of the analysis include the unreliability of
election results in establishing a causal relationship to voting
motivation. Since the far-right SVP campaigned on an anti-migration
platform, we assume that many voters were mobilised by this issue.
However, this does not account for non-voters, who represented 53% of
the population in 2023. Additionally, some people might have voted for
far-right parties for other reasons, such as fiscal austerity or
opposition to social liberty policies—sometimes referred to as “culture
war” issues. As with all elections, many people continue to vote for the
party they have identified with for years. The election data does not
provide insights into non-voters’ motivations for abstaining from voting
and, therefore, may not be representative of the entire population,
potentially leading to biased results.
Some limitations must also be noted regarding the statistical
analysis. For education levels, the BFS only publishes data per
district, not per municipality. The scarcity of data points reduces the
reliability of this indicator. Furthermore, demographic factors such as
age ratio, income per capita, and migrant population percentage can be
highly correlated, making it difficult to isolate the effect of each
variable and leading to multicollinearity. The relationship between
election results and demographic factors may not be linear, and linear
models may oversimplify these relationships. Moreover, the chosen
demographic indicators might not be conclusive, and the omission of
relevant variables can lead to biased estimates. The variance of errors
may not be constant across observations, violating one of the key
assumptions of linear regression (heteroscedasticity).
Looking at the statistical relevance, many of the regressions and
correlations (particulary in cantons with few parties and few
municipalities) are not significant. Since the statistical modelling was
not the focus of this project, the low significance of some values is
considered neglible.
The regression analysis highlights significant relationships between
demographic factors and electoral outcomes in the 2023 Swiss elections.
While secondary education levels and the percentage of non-Swiss
residents appear as the most consistent predictors of the SVP’s
electoral success, other factors, such as income per capita,
naturalisation rate, and age quota, show considerable variability
depending on the canton. The data suggests that the SVP performs
particularly well in municipalities with a high proportion of Swiss
nationals holding a secondary-level education (e.g., vocational
training) and where the percentage of non-Swiss residents is lower.
These trends indicate that the SVP’s voter base predominantly comprises
Swiss nationals without university education, residing in areas with
lower demographic diversity.
The results also highlight distinct electoral profiles of two
cantonal right-wing parties: Lega in Ticino and MCG in Geneva, which
both overlap and diverge from the SVP’s voter base. Lega’s performance
in Ticino is strongly associated with lower levels of taxable income and
secondary education, indicating that it appeals to economically weaker
municipalities with lower educational attainment. Interestingly, unlike
the SVP, Lega also correlates positively with a higher share of
non-Swiss residents and a higher naturalisation rate. This suggests that
far-right voters are not a monolithic group but come from different
communities and may be motivated by a range of issues.
Looking at the SVP’s main competitors across the political spectrum,
the Social Democratic Party (SP) performs best in urbanised
municipalities with lower and middle incomes, higher shares of migrants,
and higher levels of education. The Greens similarly benefit from these
variables, showing significant overlap with the SP’s voter base in terms
of demographic markers. Conversely, the Mitte party appeals to
middle-class voters in less ethnoculturally diverse municipalities. The
Green Liberal Party (GLP), on the other hand, finds its support in
diverse urban areas with a high share of university-educated people. The
Free Democratic Party (FDP) shares a similar economically liberal
profile to the GLP but performs best in high-income municipalities with
a strong secondary education presence, suggesting a voter base of SMEs
and middle-class professionals, such as self-employed
entrepreneurs.
Despite clear trends and patterns, it remains difficult to establish
causality between demographic factors and voting outcomes. While the SVP
campaigned heavily on an anti-migration platform, it is impossible to
determine whether voters were primarily mobilised by this issue. Many
voters may have supported the party for other reasons, such as economic
policies, opposition to social liberalism, or long-term partisan
loyalty. Additionally, the analysis does not account for non-voters, who
made up a significant portion of the electorate in 2023.
Further methodological constraints arise from data availability and
statistical modelling limitations. Education levels, for instance, are
only published at the district level rather than for individual
municipalities, reducing the precision of this variable. Additionally,
demographic factors such as age, income, and migration are often highly
correlated, creating potential issues of multicollinearity that make it
difficult to isolate the independent effect of each variable. The
assumption of linear relationships between predictors and election
outcomes may also oversimplify the dynamics at play, and
heteroscedasticity may impact the reliability of the model’s
estimates.
The 2023 Swiss federal election marked a pivotal moment in the
country’s political landscape, with the Swiss People’s Party (SVP)
regaining its dominant position, securing 27.9% of the vote. The
question remains: which factors are driving far-right support, and
particularly, what role does demographic composition play in shaping
electoral outcomes? This is why the present study sought to investigate
whether ethnoculturally homogeneous regions were more likely to endorse
far-right ideologies compared to diverse, urban centres, while also
considering the importance of other demographic indicators.
The findings reveal insights into the demographic drivers of the
SVP’s electoral success. Consistently across Swiss cantons, two factors
stood out as the most robust predictors: the percentage of non-Swiss
residents and the share of the population with secondary-level
education. Municipalities with lower proportions of non-Swiss residents
and higher percentages of individuals holding vocational education
degrees exhibited significantly stronger support for the SVP. This
suggests that the party’s messaging resonates most effectively in
regions characterised by ethnocultural homogeneity and among voters
without university-level education. In contrast, areas with higher
shares of foreign-born residents, higher naturalisation rates, and
greater educational attainment were more likely to support left-leaning
parties, such as the Social Democratic Party (SP) and the Greens,
reinforcing the hypothesis that diversity correlates with lower
far-right support.
However, the study also uncovered notable regional variations and
complex relationships between demographic markers and party support.
While secondary education and non-Swiss population share were consistent
predictors, factors such as income per capita, age distribution, and
naturalisation rates varied across cantons. The analysis relied on
correlation rather than causation, meaning that while strong
associations were identified, the exact motivations driving voter
behaviour remain somewhat unclear. Moreover, potential multicollinearity
among demographic factors and the lack of research into non-voters’
perceptions of the 2023 elections make it difficult to reliably
determine the extent to which public opinion on migration has shifted.
It is also worth noting that while the SVP centred its campaign on an
anti-migration platform, other issues—such as economic concerns,
cultural conservatism, and long-term partisan loyalty—may have also
influenced voting patterns.
In conclusion, despite some limitations, the results underscore the
importance of demographic composition in shaping the success of
far-right politics in Switzerland. Ethnoculturally homogeneous regions,
particularly those with lower educational levels and fewer foreign
residents, appear to be fertile ground for the SVP’s political agenda.
These insights offer valuable guidance for this study’s clients, such as
consultants, competing parties, and organisations seeking to counter
political polarisation and promote inclusive democratic participation.
By considering the social and economic contexts that drive far-right
support, stakeholders can craft more effective outreach strategies,
address voter anxieties before polarisation or radicalisation emerge,
and foster a more balanced political discourse in the future.